CogIMon

Supported by

Media

TUBS-EKUT: collaboration on Control of Whole-body Robot Motion for Psychotherapeutic Juggling in VR

On Feb. 2017, EKUT had a visit at TUBS in order to intensively follow the collaboration on the throwing scenario (T.6.1) and development on Orocos-Gazebo-Unity framework and progress on using Virtual Reality in the experiment.

Date: 26.03.18

IIT: collaboration between the EU projects CogIMon and AnDy

Dr. Enrico Mingo was invited at INRIA visiting Dr. Serena Ivaldi’s group, to collaborate on the topic of compliant whole-body control for humanoid robots involved in human-robot collaboration tasks. This collaboration started considering some common topics shared by the CogIMon project and the AnDy EU project. The aim was to implement a whole-body compliant stabilizer using the control framework OpenSoT (developed by IIT and used in the CogIMon project) for the iCub platform, used inside the AnDy project. The integration was successfullly tested for three main tasks: i) whole-body squat, ii) whole-body bi-manual manipulation control using interactive markers and iii) whole-body compliance during external perturbation while carrying an object. The achieved results prove the benefit of the software tools and algorithms developed inside the CogIMon project for the robotics community. Possible future collaborations and publications has also been considered starting from this first achievements.

TUBS-UEDIN-UniBi: Decoupled Motion and Force Control for Underactuated Robots

Multi-arm manipulation scenario: An intensive collaboration within the CogIMon project between TU-Braunschweig (IRP), University of Edinburgh (UEDIN) and University of Bielefeld (UniBi) has ended to a nice demo using 4 KUKA robots. The results have been submitted to ICRA 2018.

EPFL-BioRob: How robots and humans learn to carry things together

EPFL-BioRob Interview with Digital Trends: Greater body awareness may make COMAN an ace at helping with everyday tasks

Here is a nice interview of Digital Trends with our CogIMon project colleagues at EPFL-BioRob, Dr. Hamed Razavi and Jessica Lanini (working under the superviosion of Prof. Auke Ijspeert). Some of the results of their ongoing research has been published as a Journal paper at http://journals.plos.org....

EPFL-LASA team was selected as one of the five finalists in KUKA Innovation Award 2017

The researchers from EPFL-LASA, Sina Mirrazavi, Nadi Figueroa, Aude Billard, were selected as one of the five finalist in KUKA Innovation Award 2017. They demonstrated their concept for two robots that cooperate with each other and collaborate with the human operator – for example, to move heavy parts such as car doors without the risk of a collision and to support the human operator in positioning these parts correctly.

Projected inverse dynamics control for wiping a board with a single Kuka arm

UniBi/TUBS and BRHM started to build the first components of a so-called Gazebo-Orocos simulation framework by re-implementing the projected inverse dynamics control approach for wiping a board with a single Kuka robot arm based on a recent paper by BRHM.

Multiple task optimization with a mixture of controllers for motion generation

This video shows the simulated motions for the virtual 3 DOF pendulum and the humanoid robot COMAN. The initial mixture coefficients are compared to optimized coefficients for new reaching targets that were not part of the optimization process.

Continuous Task-Priority Rearrangement during Motion Execution with a Mixture of Torque Controllers

This video shows the simulated motions for the humanoid robot COMAN. The initial mixture coefficients are compared to optimized coefficients for compensating external forces and task-priority rearrangement.

Soft catching an object in flight

Catching a fast flying object is particularly challenging as consists of two tasks: it requires extremely precise estimation of the object's motion and control of the robot motion. Any small imprecision may lead the fingers to close too abruptly and let the object fly away from the hand before closing. We present a strategy to overcome for sensori-motor imprecision by introducing softness in the catching approach. Soft catching consists of having the robot moves with the object for a short period of time, so as to leave more time for the fingers to close on the object. We use a dynamical systems (DS) based control law to generate the appropriate reach and follow motion, which is expressed as a Linear Parameter Varying (LPV) system. We propose a method to approximate the parameters of LPV systems using Gaussian Mixture Models, based on a set of kinematically feasible demonstrations generated by an off-line optimal control framework. We show theoretically that the resulting DS will intercept the object at the intercept point, at the right time with the desired velocity direction.

Coordinated multi-arm motion planning: Reaching for moving objects in the face of uncertainty

Coordinated control strategies for multi-robot systems are necessary for tasks that cannot be executed by a single robot. This encompasses tasks where the workspace of the robot is too small or where the load is too heavy for one robot to handle. Using multiple robots makes the task feasible by extending the workspace and/or increase the payload of the overall robotic system. In this paper, we consider two instances of such task: a co-worker scenario in which a human hands over a large object to a robot; intercepting a large flying object. The problem is made difficult as the pick-up/intercept motions must take place while the object is in motion and because the object's motion is not deterministic. The challenge is then to adapt the motion of the robotic arms in coordination with one another and with the object. Determining the pick-up/intercept point is done by taking into account the workspace of the multi-arm system and is continuously recomputed to adapt to change in the object's trajectory. We propose a dynamical systems (DS) based control law to generate autonomous and synchronized motions for a multi-arm robot system in the task of reaching for a moving object. We show theoretically that the resulting DS coordinates the motion of the robots with each other and with the object, while the system remains stable. We validate our approach on a dual-arm robotic system and demonstrate that it can re-synchronize and adapt the motion of each arm in synchrony in a fraction of seconds, even when the motion of the object is fast and not accurately predictable.